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NASA NTRSDate not supplied

Recent Advancements in the PATO Material Response Code

Introduction: Predicting the complicated multiphysics phenomena during atmospheric entry requires high-fidelity modeling tools to refine estimates of mission risks during entry. To this end, new capabilities are being added to the Porous-material Analysis Toolbox based on OpenFOAM (PATO). PATO is an open-source software for Computational Material Response (CMR) of reactive porous materials submitted to high-temperature environments. The objective of this work is to highlight current efforts to add to and improve upon the modeling capabilities of PATO. These include efforts to loosely couple PATO with other discipline specialized codes including hypersonic Computational Fluid Dynamics (CFD), to assess the interaction effects between pyrolysis gas blowing and the boundary layer, and Computational Solid Mechanics (CSM), to address modeling of mechanical erosion. Other refinements include surface phenomena modeling capabilities to address the effects of silicone-based coatings applied to the TPS during flight preparation, and a unified multiphase solver for a mixed porous-material and plain-fluid domain. Coupling CMR with CFD (CMR/CFD): A loose coupling between PATO and the Data Parallel Line Relaxation (DPLR) CFD code has been achieved by making use of a blowing boundary condition at the heatshield surface available in DPLR. Starting with heat flux estimates with no pyrolysis gas blowing at the surface, blowing gases are computed by the CMR and passed to the CFD such that aerothermal properties of the environment can be recomputed for a new CMR computation. This leads to an iterative process which is supplemented with an estimate of the radiative heat flux using the Nonequilibrium air radiation (NEQAIR) program. The entire iterative process is illustrated in Figure 1. This coupling strategy has been utilized in computing the MSL material response. The goal is to compare the coupled CMR/CFD results with material response results obtained using traditional blowing corrections. Coupling CMS with CMR: A mechanical erosion model is currently being implemented in PATO to account for the additional mass removal induced by high shear conditions. The modeling process at each timestep consists of updating the mechanical properties as a function of temperature and computing the stress tensor and displacement fields of the material. Then, a failure criteria model determines the regions in which the stress exceeds the ultimate strength values resulting in mesh movement to account for mass removal. This model allows the material response simulation to compute the recession due to both oxidation and shear-induced erosion. The model is demonstrated by computing material response of sphere-cone arc jet samples. Surface Modeling Capabilities: NuSil, a silicone-based coating, was sprayed onto the MSL and Mars 2020 heatshields to mitigate shedding of phenolic dust. To better understand the effects of the NuSil coating on the material response, a novel model has been implemented in PATO. In this model, the equilibrium of the charred NuSil surface is modeled as pure silica, and a constant offset, inspired by the classical spallation model, is added to the the char blowing rate and wall enthalpy to reproduce HyMETS experimental results. The model has also been used to estimate the 3D material response of the MSL heatshield. Unified Solver: In addition to the iterative loose coupling approach mentioned above, a multiphase unified solver is being developed to couple the environment (plain-fluid phase) and the porous-material phase. The solver is based on the volume averaged conservation of mass, momentum, and energy for the macroscale with closure models which include microscale effects through effective physicochemical properties. The unified solver has been used to compute flow through a porous plug and solve the Beavers and Joseph problem. Since the strong coupling between phases is inherent to this solver, modeling assumptions present in other coupling methods of material response are mitigated. This strategy also makes it feasible to capture the competition between surface and volume ablation in the same computational domain, which is usually not possible with other coupling approaches.

Material Response↗
NASA NTRSDate not supplied

Recent Advancements in the PATO Material Response Code

Introduction: Predicting the complicated multiphysics phenomena during atmospheric entry requires high-fidelity modeling tools to refine estimates of mission risks during entry. To this end, new capabilities are being added to the Porous-material Analysis Toolbox based on OpenFOAM (PATO) [1,2,3]. PATO is an open-source software for Computational Material Response (CMR) of reactive porous materials submitted to high-temperature environments. The objective of this work is to highlight current efforts to add to and improve upon the modeling capabilities of PATO. These include efforts to loosely couple PATO with other discipline specialized codes including hypersonic Computational Fluid Dynamics (CFD), to assess the interaction effects between pyrolysis gas blowing and the boundary layer, and Computational Solid Mechanics (CSM), to address modeling of mechanical erosion. Other refinements include surface phenomena modeling capabilities to address the effects of silicone-based coatings applied to the TPS during flight preparation, and a unified multiphase solver for a mixed porous-material and plain-fluid domain. Coupling CMR with CFD (CMR/CFD): A loose coupling between PATO and the Data Parallel Line Relaxation (DPLR) [4] CFD code has been achieved by making use of a blowing boundary condition at the heatshield surface available in DPLR. Starting with heat flux estimates with no pyrolysis gas blowing at the surface, blowing gases are computed by the CMR and passed to the CFD such that aerothermal properties of the environment can be recomputed for a new CMR computation. This leads to an iterative process which is supplemented with an estimate of the radiative heat flux using the Nonequilibrium air radiation (NEQAIR) [5] program. The entire iterative process is illustrated in Figure 1. This coupling strategy has been utilized in computing the MSL material response. The goal is to compare the coupled CMR/CFD results with material response results obtained using traditional blowing corrections. Coupling CMS with CMR: A mechanical erosion model is currently being implemented in PATO to account for the additional mass removal induced by high shear conditions. The modeling process at each timestep consists of updating the mechanical properties as a function of temperature and computing the stress tensor and displacement fields of the material. Then, a failure criteria model determines the regions in which the stress exceeds the ultimate strength values resulting in mesh movement to account for mass removal. This model allows the material response simulation to compute the recession due to both oxidation and shear-induced erosion. The model is demonstrated by computing material response of sphere-cone arc jet samples. Surface Modeling Capabilities: NuSil, a silicone-based coating, was sprayed onto the MSL and Mars 2020 heatshields to mitigate shedding of phenolic dust. To better understand the effects of the NuSil coating on the material response, a novel model has been implemented in PATO. In this model, the equilibrium of the charred NuSil surface is modeled as pure silica, and a constant offset, inspired by the classical spallation model, is added to the the char blowing rate and wall enthalpy to reproduce HyMETS experimental results. The model has also been used to estimate the 3D material response of the MSL heatshield [6]. Unified Solver: In addition to the iterative loose coupling approach mentioned above, a multiphase unified solver is being developed to couple the environment (plain-fluid phase) and the porous-material phase. The solver is based on the volume averaged conservation of mass, momentum, and energy for the macroscale with closure models which include microscale effects through effective physicochemical properties. The unified solver has been used to compute flow through a porous plug and solve the Beavers and Joseph problem [7]. Since the strong coupling between phases is inherent to this solver, modeling assumptions present in other coupling methods of material response are mitigated. This strategy also makes it feasible to capture the competition between surface and volume ablation in the same computational domain, which is usually not possible with other coupling approaches.

Thermal Protection Systems↗
NASA NTRS2022

Separating Propulsive Mass and Energy for Space Applications

Initially and traditionally, space access and in-space propulsion utilized combustion and expulsion of chemical “fuels” carried on board, with performance governed by the rocket equation. These chemicals produced energy, and after combustion, constituted the propulsive mass/momentum. The best such chemicals in terms of Isp that are deemed safe engineering/mission wise are hydrogen and oxygen, producing some 450 seconds of Isp. There are more reactive chemicals, such as fluorine, which have higher Isp, but also have serious safety issues. Propulsion in atmospheres can ingest atmospheric constituents (aka “airbreathing propulsion”) which provide additional propulsive mass when heated and a component of the combustion/energy generation process. This utilization of non-stored/carried propulsive mass provides partial separation of propulsive mass and energy and produces higher Isp. The other approach to separating propulsive mass and energy is to, either on board or added from offboard, supply additional energy, such as from nuclear or solar sources. This is the approach for fission nuclear thermal and electric propulsion, which can provide an Isp in excess of 800 seconds of Isp. Other processes or in addition to thermal expansion, such as electro-magnetics, can be employed to increase exit velocity and Isp. Separating propulsive mass and energy for space faring is commonly referred to as a means to circumvent the rocket equation and is capable of producing major benefits for the development of commercial deep space and space faring in general including affordable fast transits to mitigate the human health impacts of galactic cosmic radiation (GCR) and microG. The key to higher than chemical Isp, beyond H2-O2, and beyond the radiated energy and systems limitations of solar for space faring, is a light weight, high energy density source, either on board or via utilization of energy beaming to the vehicle. Traditional energy sources include chemical, heat, electrics, mechanical, photons, and nuclear. Propulsive mass can be carried on board, sourced beyond the surface of Earth via in-situ resource utilization (ISRU), and includes harvesting from atmospheres. High Isp via electromagnetic related propulsive mass acceleration requires sufficient ionization and conductivity. The major metrics for space propulsion are costs, safety, Isp, weight, and thrust level, the latter dependent upon mission requirements. High thrust for human missions is needed to reduce time exposed to radiation and microG and high thrust is required for space access. Costs of space access are reducing via reusability, printing manufacture, and robotization of manufacturing and operation. Chemical rockets provide high thrust from the expansion of the heated mass constituents at high mass flow. The other high thrust propulsion approach is magnetohydrodynamics (MHD), which, in addition to high thrust, has a high Isp of over 2,000 seconds of Isp. The VASIMIR engine offers some 5,000 seconds of Isp at high thrust [ref. 3]. Electric propulsion cycles are capable of Isp much higher than that but at low thrust levels using available energy sources. With cost as a major metric, reusable rockets and improved manufacturing and operations are rapidly greatly lowering the costs of space access, which could provide/supply in space fuel depots and affordable chemical propulsion for the desired human fast transits (e.g., some 200-day round trips to Mars). The other option for fast transits is VASIMIR, given a nuclear on-board energy source that has the requisite many megawatts of power and an alpha, kgs of weight/kW of energy produced, on the order of one (i.e., a light weight, high energy density energy source). The purpose of this report is to examine the options beyond traditional rocket engines, where propulsive mass and energy are combined, specifically the separation of propulsive mass and energy. This report considers the spectrum of advanced energetics, sources of propulsive mass, conductivity enhancement approaches, energy beaming possibilities, and candidate propulsion cycles. Suggestions are made for various combinatorial, system level, beyond traditional combustion rocket, space propulsion approaches for human deep space missions given the changing conditions of increased knowledge of deep space resources for ISRU, revolutionary energetics, and technology advancements writ large.

Dennis M. Bushnell↗
NASA NTRSDate not supplied

A Machine Learning Approach to Improve Air Traffic Management Initiatives

Collaborating closely with commercial air carriers and related organizations, the Federal Aviation Administration(FAA) regulates air traffic and ensures the safety and efficiency of air operations. Air traffic controllers make strategic decisions, such as delaying, rerouting, or canceling flights, partly based on guidance provided by the FAA’s Air TrafficControl System Command Center (ATCSCC). The guidance includes, among other things, control measures known asTraffic Management Initiatives (TMIs) designed to enhance safety and improve operational efficiency. TMIs play a crucial role in managing the demand and capacity within the U.S. National Airspace System (NAS). Two major TMIs that are routinely used (primarily to mitigate the adverse effects of bad weather) are Ground Delay Programs (GDPs) andGround Stops (GSs). In a GDP, flights destined for airports facing thunderstorm activity experience delays at their origin airports. This proactive approach minimizes the risk of routing aircraft through hazardous weather conditions and also replaces (fuel burning) airborne delays with ground delays. In a GS, a temporary restriction is imposed on the departure or arrival of aircraft at a specific airport or within a designated airspace. Although other TMIs (e.g., miles-in-trail) are also implemented as part of (air) traffic flow management in the NAS, the focus of this work is on GDPs and GSs. Since TMIs, by design, lead to flight delays or cancellations, it is crucial to put in place the right set of parameters(e.g., scope and duration of the GDP). For example, when the end time of a GDP extends beyond what is necessary, it imposes unnecessary delays on departing flights. This situation could occur as a result of inaccurate prediction of the(required) duration of the GDP based on the weather forecast. On the other hand, if a GDP ends prematurely before the underlying capacity constraints are resolved at the destination airport, it may result in airborne holding. The delicate balance lies in matching the termination of the GDP precisely with the resolution of capacity constraints, avoiding both the imposition of unnecessary ground delays and the need for airborne holding due to premature program termination.Failing to specify the right parameters for TMIs also leads to flight delays, creating a significant obstacle in managing the increasing traffic volumes causing increased work load for the controllers. To address this issue, we propose the integration of Machine Learning (ML) models in the traffic flow management(TFM) pipeline. In current operations, decisions are made by human experts based on extensive training, historical patterns, available traffic and weather data. Since we have an abundance of data from past events that tell us the likely impact of various TMIs, by ingesting historical data, properly trained ML models can offer valuable insights and aid human decision-making. With the FAA increasingly exploring advanced analytics, ML emerges as a focal point for enhancing TFM within the National Airspace System (NAS). As a first step, this study aims to provide traffic controllers with decision-making support for the issuance and adjustment of TMIs. Data analytics and machine learning have been previously employed to address some of the challenges associated with TMIs. Numerous studies have concentrated on various facets of TMI issuance, exploring factors influencing TMI parameters, including arrival rate, airport capacity, and delay prediction. For example, using weather forecasts, several statistical methods were used to produce probabilistic capacity profiles which in conjunction with deterministic models provided insights into the GDP planning process [1–4]. The downside of using deterministic models is that they rely on fixed inputs and predetermined rules, which lack the ability to account for the inherent uncertainty and variability present in real-world scenarios. In a separate series of studies, researchers aimed to predict the occurrences of GDPs and GSs. The majority of these studies utilized various supervised learning methods, including Decision Trees, Naive Bayes, Support VectorMachines, and Random Forests to analyze the influence of weather conditions and arrival demand on TMI incidents[5–8]. However, these studies primarily focused on predicting the incidence of TMIs without explicitly addressing the scope of TMIs, including their duration and their geographical coverage. Furthermore, the emphasis of these studies was largely on GDPs, given their higher frequency and longer duration when compared to GSs. A limited number of studies focused on predicting the parameters of TMIs, specifically addressing their duration and extent. In one such study focusing on optimizing the TMI parameters at San Francisco International Airport (SFO),the authors utilized a probabilistic forecast of fog [9]. They simulated various capacity scenarios based on the (fog)burn-off forecasts, selecting GDP parameters that minimized airborne and overall ground delays. However, this approach exclusively emphasizes stratus (fog) burn-off as the primary determinant of GDP and GS, neglecting other influential factors like severe weather events, runway closures, lower capacity than traffic demand, and other important variables. Given the complexity of predicting the TMI and determining its scope, we seek a more holistic approach. We aim to consider all significant factors that could impact TMIs and their parameters. What sets this research apart is the fusion of all data sources relevant to the issuance and adjustment of TMIs and it represents the first comprehensive attempt to optimize TMIs in this manner. Since this comprehensive solution involves various aspects, we break down the problem into smaller components and input all parameters into a unified model called the “TMI Adjuster”. Figure 1 shows the overall framework and the list of datasets used in each model. The objective of the TMI Adjuster module is to deliver reliable, consistent and expedited recommendations for the progression, adjustment, and termination of TMIs. The ML solution entails developing a pipeline capable of predicting the necessity of a TMI (e.g., GS or GDP) along with its various parameters. For example, in the case of a GS, this includes the scope of the GS either in terms of distance from the destination airport or based on pre-defined airspace sectors. Here, scope refers to those regions and departing airports that are subject to the GS. In this paper, we concentrate on the issuance of GSs in the three major airports in the New York area — LaGuardia(LGA), John F. Kennedy International (JFK), and Newark Liberty International (EWR). We fuse traffic, weather and other relevant aviation data from years 2017 to 2019 to train and validate the ML models. In particular, we use the following datasets: •Terminal Aerodrome Forecast (TAF): meteorological forecasts specific to each airport, issued four times a day, covering predefined time periods. •TMI data: includes all GSs and GDPs along with their respective parameters. •Aviation System Performance Metrics (ASPM): includes traffic related data such as aircraft delays, arrival, and departure rates. •Notices to Airmen (NOTAMs): utilized to extract runway closure data and manage interdependencies between terminals in close proximity. •Flight cancellation data •Airspace Flow Programs (AFP): includes information on flight airborne holdings caused by TMIs. The data preprocessing entails transforming ASPM, TMI, AFP, NOTAMs, and weather data into an hourly format and consolidating all datasets by merging them based on date and time as the primary key. The TMI Adjuster framework comprises two parallel models: one dedicated to GS and a second model focused on GDP. As previously mentioned, our specific focus is on the GS model as a multi-classification problem. In this framework, each data point of the GS model input summarizes ten hours of data. Specifically, the data loader for the GS model generates the input and output of the model as follows: at a given time step, the input includes the actual traffic, weather, and TMI data from the two-hour window before the time step, alongside the weather forecast and scheduled traffic for the next 8 hours starting from the time step. Based on this information, the output of the GS model for each time interval consists of three dimensions. The first dimension represents a binary decision on whether there should be a GS in place for the next hour or not. The second dimension is related to the scope of the GS in the United States, and the third dimension is related to the scope of the GS in Canada (i.e., to determine if the GS impacts airports in Canada).One of the challenges with TMI modeling is the sparsity of TMI events, particularly regarding its scope. To address this challenge in the scope of the GS model output, we implement grouping. The GS scope for the US region is defined based on a list of centers that should be included when the GS is in place. With 20 centers in the US, we utilized historical data to group them into 4 categories. In particular, we summarized our historical data in a graph format where nodes represent centers, and link weights are defined based on the co-occurrence of centers in the scope parameter ofTMIs. By identified strongly connected components in this graph, we were able to partition the centers into four groups. We consider two model structures for the GS Model. Firstly, a hierarchical classification model [10], where the human decision-making for a GS is of hierarchical nature. The decision-maker first decides whether there is a need fora GS, and if the answer is yes, determines the scope. A hierarchical classification model organizes the problem into a class hierarchy, typically a tree or a Directed Acyclic Graph (DAG) structure, and considers the dependency of the decision in the previous step to the next component [10]. Here, we employ the local classifier per level approach, which involves training one multi-class classifier for each level of the class hierarchy. The second structure is the independent structure. In this setting, as the name suggests, we do not consider the dependency of the decisions in the different dimensions of the output of the model. Instead, for each dimension, we train a multi-class classifier independently. Table 1 summarizes GS model statistics for training, validation and testing. The table documents the effect of limiting data to the time steps when there was actually a TMI in place or when a TMI had just terminated. This resulted in a more balanced distribution of the GS class(GS positive class)versus “No GS”(GS negative class), which might help the training process. While JFK and LGA follow very similar distributions, with 40% and 42% GS positive class respectively, EWR has proportionally fewer GS incidents at 28%. Our subsequent phase involves evaluating the performance of both hierarchical structure and independent structure using different state-of-the-art multi-class classifier models such as Random Forest, Decision Trees, K-nearest Neighbors, and Logistic Regression and forecast the duration and scope of the GSs.

Farzan Masrour Shalmani↗
NASA NTRS2015

Human System Risk Management for Space Flight

This brief abstract reviews the development of the current day approach to human system risk management for space flight and the development of the critical components of this process over the past few years. The human system risk management process now provides a comprehensive assessment of each human system risk by design reference mission (DRM) and is evaluated not only for mission success but also for long‐term health impacts for the astronauts. The discipline of bioastronautics is the study of the biological and medical effects of space flight on humans. In 1997, the Space Life Sciences Directorate (SLSD) initiated the Bioastronautics Roadmap (Roadmap) as the "Critical Path Roadmap", and in 1998 participation in the roadmap was expanded to include the National Space Biomedical Research Institute (NSBRI) and the external community. A total of 55 risks and 250 questions were identified and prioritized and in 2000, the Roadmap was base-lined and put under configuration control. The Roadmap took into account several major advisory committee reviews including the Institute of Medicine (IOM) "Safe Passage: Astronaut care for Exploration Missions", 2001. Subsequently, three collaborating organizations at NASA HQ (Chief Health and Medical Officer, Office of Space Flight and Office of Biological & Physical Research), published the Bioastronautics Strategy in 2003, that identified the human as a "critical subsystem of space flight" and noted that "tolerance limits and safe operating bands must be established" to enable human space flight. These offices also requested a review by the IOM of the Roadmap and that review was published in October 2005 as "A Risk Reduction Strategy for Human Exploration of Space: A Review of NASA's Bioastronautics Roadmap", that noted several strengths and weaknesses of the Roadmap and made several recommendations. In parallel with the development of the Roadmap, the Office of the Chief Health and Medical Officer (OCHMO) began a process in 2004 of evaluating the tolerance limits and safe operating bands called for in the Bioastronautics Strategy. Over the next several years, the concept of the "operating bands" were turned into Space Flight Human System Standards (SFHSS), developed by the technical resources of the SLSD at the NASA Johnson Space Center (JSC). These standards were developed and reviewed at the SLSD and then presented to the OCHMO for acceptance. The first set of standards was published in 2007 as the NASA‐STD‐3001, Volume 1, Crew Health that elaborated standards for several physiological areas such as cardiovascular, musculoskeletal, radiation exposure and nutrition. Volume 2, Human Factors, Habitability and Human Health was published in 2011, along with development guidance in the Human Integration Design Handbook (HIDH). Taken together, the SFHSS Volumes 1 and 2, and the HIDH replaced the NASA‐STD‐3000 with new standards and revisions of the older document. Three other changes were also taking place that facilitated the development of the human system risk management approach. In 2005, the life sciences research and development portfolio underwent a comprehensive review through the Exploration Systems Architecture Study (ESAS) that resulted in the reformulation of the Bioastronautics Program into Human Research Program (HRP) that was focused on appropriate mitigation results for high priority human health risks. The baseline HRP budget was established in August 2005. In addition, the OCHMO formulated the Health and Medical Technical Authority (HMTA) in 2006 that established the position of the Chief Medical Officer (CMO) at the NASA JSC along with other key technical disciplines, and the OCHMO became the responsible office for the SFHSS as noted above. The final change was the establishment in 2008 of the Human System Risk Board (HSRB), chaired by the CMO with representation from the HRP, SLSD management and technical experts. The HSRB then began to review all human system risks, established a comprehensive risk management and configuration management plan and data sharing policy. These major developments of standards, the HRP, the HMTA and a forum for review of human system risks (HSRB) facilitated the integration of human research, medical operations, systems engineering and many other disciplines in the comprehensive review of human system risks. The HSRB began a comprehensive review of all potential inflight medical conditions and events and over the course of several reviews consolidated the number of human system risks to 30 where the greatest emphasis is placed for investing program dollars for risk mitigation. The HSRB considers all available evidence from human research, medical operations and occupational surveillance in assessing the risks for appropriate mitigation and future work. All applicable DRMs (low earth orbit 6 and 12 months, deep space sortie for 30 days and 1 year, a one year lunar mission, and a planetary mission for 3 years) are considered as human system risks are modified by the hazards associated with space flight such as microgravity, exposure to radiation, distance from the earth, isolation and a closed environment. Each risk has a summary assessment representing the state of knowledge/evidence base for that risk, the available risk mitigations, traceability to the SFHSS and program requirements, and future work required. These data then can drive coordinated budgets across the HRP, the International Space Station, Crew Health and Safety and Advanced Exploration System budgets. These risk assessments were completed for 6 DRMs in December of 2014 and serve as the baseline for which subsequent research and technology development and crew health care portfolios can be assessed. The HSRB will review each risk at least annually and especially when new information is available that must be considered for effective risk mitigation. The current status of each risk can be reported to program management for operations, budget reviews and general oversight of the human system risk management program.

Davis, Jeffrey↗
NASA NTRSDate not supplied

An Overview of Experiments and Modeling of Polysiloxane-Coated Thermal Protection Systems for Missions to Mars, Titan, and Beyond.

Phenolic Impregnated Carbon Ablator (PICA) gained heritage during the historic Stardust mission, where it successfully returned samples from a comet’s tail and has since been instrumental in delivering payloads to the surface of Mars [1-3]. Most recently, PICA enabled the safe return of samples collected from asteroid Bennu as part of the OSIRIS-REx mission. This rich legacy underscores PICA’s critical role in allowing NASA’s most ambitious exploration missions. However, the friable nature of its phenolic phase presents challenges during handling and pre-launch activities. To mitigate this issue, PICA is coated with a polysiloxane resin system, which serves to suppress particulate dispersion and thereby safeguard spacecraft components. A comprehensive understanding of the polysiloxane resin’s behavior is imperative, as it profoundly shapes the material response of PICA during atmospheric entry by influencing its thermal and oxidative stability. This influence extends to thermocouple plugs embedded within thermal protection systems. These plugs have demonstrated their significance in missions such as Mars Science Laboratory (MSL) and Mars 2020, where the MEDLI and MEDLI2 instrumentation suites delivered in-valuable insights into the performance of thermal protection systems during entry into the Martian atmosphere [4]. Looking ahead, missions such as Dragonfly, set to descend into Titan’s dense atmosphere, aim to leverage advanced sensor technologies to further refine our understanding of thermal protection response [5]. Moreover, thermocouple plugs play an essential role in validating cutting-edge material response models, such as those pioneered under NASA’s Entry Systems Modeling Project (ESM), designed, in-part, to predict the operational integrity of thermal protection systems under the extreme stresses of atmospheric entry. To achieve these modeling goals, ground-based experiments are crucial to provide the foundational data necessary for developing and refining these predictive tools. To this end, an extensive test campaign was conducted at the Hypersonic Materials Environmental Test System (HyMETS) to investigate the high-temperature behavior of the polysiloxane resin in an air environment [6]. These experiments revealed critical phenomena, including the formation of a silicon oxycarbide layer that enhances oxidation resistance, moderates surface temperatures, and alters in-depth thermal response. Building on these findings, subsequent tests were designed to simulate atmospheric entry conditions in reactive gases, such as CO2 and N2, to mimic the environments of Mars and Titan, respectively, as well as non-reactive gases representing the atmospheres of the Ice Giants (Neptune and Uranus). A heating rate dependent decomposition mechanism has been identified for the polysiloxane resin under oxidizing conditions (Fig. 1). In the initial stage, the resin and the underlying thermal protection system undergo pyrolysis, rapidly generating a thin amorphous silicon oxycarbide interwoven with carbonaceous char and residual fibers from PICA. During the second stage, the nascent oxide layer establishes a robust, oxidation-resistant thermal barrier coating, which significantly impedes heat transfer to the underlying carbonaceous char, resulting in a stagnation of the surface temperature. A key factor contributing to this thermal resistance is the low recombination efficiency of atomic oxygen (γ), which further diminishes the heat load on the material’s interior layers [7]. Moreover, as the surface temperature stagnates, the silicon oxycarbide phase separates into distinct regions of silica and free graphite. Ultimately, when the heat flux reaches a critical threshold, a third stage is triggered, leading to the breakdown of the coating through carbothermal reduction, exposing the underlying char layer. This exposure leads to a dramatic surface temperature spike, driven by highly exothermic reactions between atomic oxygen and the char layer, further accelerating material degradation. A detailed mass and heat transfer model of PICA coated with polysiloxane resin was implemented in the Porous material Analysis Toolbox based on OpenFOAM, PATO [8]. The initial stage was considered negligible in this model because the resin decomposition occurs rapidly within a thin surface layer. Instead, the coating was directly considered as an oxygen-resistant thermal barrier coating. For the second stage, the thin amorphous silicon oxycarbide was treated as a pure silica surface to simplify the thermochemical behavior. The model ac-counts for surface equilibrium processes using representative elements of the coating-environment system. For the third stage, specific boundary conditions were developed to estimate the onset and progression of the coating removal. Two-dimensional material response simulations were conducted to compare uncoated and coated PICA using boundary conditions calibrated with HyMETS data. Fig. 2 illustrates that the simulations closely align with experimental data, successfully reproducing measured temperature profiles. This work will include the latest advancements in the coating model, including the calibration of recombination of atomic oxygen at the surface during the second phase. These simulated results will be further validated against additional CO2 data points from HyMETS, reinforcing the models’ predictive capabilities. These mechanisms and their effects on thermal protection systems, including thermochemical behavior and thermocouple probe performance in extreme environments, provide crucial insights for optimizing spacecraft designs that safeguard scientific payload and ensure mission success in future planetary exploration endeavors.

Active Oxidation↗
NASA NTRSDate not supplied

Metallic Environmentally Resistant Coating Rapid Innovation Initiative

Lightweight alloys such as aluminum (Al) and titanium (Ti) are often specified for space systems to minimize mass while maintaining structural integrity [1,2]. Such alloys however, have poor tribological response (high friction and wear), especially in extreme space environments, which becomes worse with the additional presence of lunar regolith. This leads to short lifetimes and premature failures that will ultimately limit long term operations on the lunar surface [2]. This project is addressing this technology gap by developing advanced wear- and radiation-resistant coatings for lightweight parts to extend the life-time and sustainability of both lunar and Martian assets. Ceramics were considered for their high wear resistance, but were rejected because of their low fracture toughness, which would be especially problematic for structural components. The novel and existing coating technologies and deposition methods are being tested in this project. The coating materials are Boron Nitride-Aluminum (BN-Al), Nickle Titanium (NiTi), Aluminum Oxide (AlO3), Ti64 with hBN at 2 and 10 vol percent (Ti-2vol%hBN and Ti-10vol%hBN), and the deposition techniques are high pressure cold-spray (CS) and ambient and vacuum plasma-spray (APS and VPS) [3,4,5]. BN-Al, NiTi, Ti-2vol%hBN, and Ti-10vol%hBN were applied with all three deposition techniques, and AlO3 was applied only using the APS deposition technique. A tungstenite (WS2) film was applied to the NiTi VPS coating. The coating and deposition technique configurations are being tested against several key end-use performance parameters. The parameters include the capabilities of the coatings under wear environments such as regolith simulant, thermal cycling from high (120°C) to cryogenic (-173°C), high vacuum (~10-7 torr), and pre- and post-exposure to ionizing particle radiation. Wear tests include pin on disk, three-body abrasion, and surface erosion by high velocity regolith impacts. The initial down selection is being performed using on virgin and environmental exposure samples using pin on disk and three body abrasion wear tests. From preliminary assessment of this testing, a few configurations were eliminated. Ti-10vol%hBN could not be applied using CS application and NiTi could not be applied using APS or CS application so these configurations did not get tested. Early pin on disk wear tests showed poor wear performance of BN-Al so this coating was eliminated early in the testing process. AlO3 on Al substrate did not survive thermal cycling, but wear testing is continuing for AlO3 on Ti substrate. A more detailed analysis is being con-ducted to further reduce the number of configuration for phase II and III. Phase II testing will include conventionally and additively manufactured substrates with surface erosion testing. In phase III, the coatings will be applied to three mechanism types: channel and slot, ball and socket, and a hinge joint. Each will demonstrate a different type of wear incidence. The mechanisms and their base materials are of direct interest to the end users and infusion points: the Human Landing System (HLS) and the Lunar Surface Innovation Initiative (sustained lunar surface operations). The technology development project is based out of Marshall Space Flight Center, has partnered with Florida International University (Miami, FL) and Plasma Processes (Huntsville, Al), and is supported by a group of NASA mentors from different centers.

dust mitigation↗
NASA NTRSDate not supplied

Chemical Reactivity of In-Situ Lunar Dust for Biotoxicity Assessment

Introduction: How does the chemical reactivity of in-situ lunar dust compare to Apollo samples currently stored in curation facilities here on Earth? Essential investigations of this question will help us to further mitigate exploration risks for future human explorers on the Moon and will also provide critical information for astrobiologists and space biologists using the Moon for scientific inquiry. Discussion: Apollo 14 dust biotoxicity studies, carried out by the NASA Lunar Airborne Dust Toxici-ty Assessment Group (LADTAG), included numerous physiochemical studies[1] and cellular and animal ex-periments. Intratracheal instillation [2] and inhalation studies [3] in rats both showed Apollo 14 dust to be intermediate in toxicity compared to low-tox titanium dusts and high-tox quartz dusts of similar particle siz-es. The collective results were used in models [4] to establish a safe exposure limit for astronauts [5]. Alt-hough LADTAG took extensive steps to preserve what chemical reactivity may still have existed in the sam-ples, it is simply unknown if they possessed true in-situ chemical reactivity or if that reactivity has de-cayed. Initial gas loss on collection and other altera-tions, and even intermittent exposure to Earth-normal conditions during subsequent decades of handling, obscure a forensic reconstruction of the initial state. Because a mineral dust’s chemical reactivity influ-ences its biotoxicity [6], researchers have developed methods to “activate” lunar dust and simulants [7][8]. Past studies that modeled impact processes and radia-tion [9] in the lunar environment suggest that in-situ lunar dust is likely to be more chemically reactive than Earth-exposed samples. Because of these results, in-situ measurements are warranted [10]. Other studies have examined the hydroxyl generating capability of iron bearing mineral phases [11][12] and further em-phasize the role iron plays in chemical reactivity of lunar material, as well as decay of chemical reactivity in mineral dusts [12]. Recent observations of the lunar surface reveal the presence of hematite [13], a finding that further supports the hypothesis that in-situ lunar dust is reactive. Since the lunar surface is heterogene-ous, dust biotoxicity is expected to vary from site to site [14] due to particle size, mineralogy, physical characteristics, degree of space weathering, and chemi-cal reactivity (Figure 1). This circumstance dictates dust assessments at a suite of lunar sites enabled by upcoming NASA and commercial lunar payload ser-vices (CLPS) opportunities. Dose, location, and dura-tion of particle exposure will also affect biological responses. In-situ chemical reactivity measurements can inform cross-cutting collaborative research cam-paigns such as astrobiology studies examining regolith interactions with organisms and its ability to preserve chemical and structural biomarkers, as well as space biology investigations that examine regolith-microbe interactions relating to life support systems, plant growth, biomining, and development of regolith bio-composites. Figure 1: Environment conditions on the lunar surface that may alter regolith reactivity. Summary A series of in-situ measurements of lu-nar dust free radical chemistry at future Artemis and CLPS landing sites, combined with LADTAG-like studies of freshly collected lunar dust specimens, will reveal the true chemical reactivity of in-situ lunar dust and generate scientific data that can be compared to the chemical reactivity and biotoxicity of samples from Apollo landing sites. Furthermore, results from in situ measurements and biotoxicity studies of freshly col-lected specimens can also be used to validate, or re-quire revision of, the current astronaut permissible exposure limit [15]. References: [1] McKay D et al (2015), Acta As-tronaut 107:163–176. [2] Rask J et al (2013), LPSC, p 3062. [3] Lam CW et al (2013), Inhal Toxicol 25:661–678. [4] James JT, et. al. (2013) , Inhal Toxicol 25:243–256. [5] Scully RR, et.al. (2013), Inhal Toxi-col 25:785–793. [6] Porter, D. W., et.al., (2002), Tox-icology 175, 63–71. [7] Wallace WT, et.al., (2009), Meteorit Planet Sci 44:961–970. [8] Wallace WT, et.al., (2010), Earth Planet Sci Lett 295:571–577. [9] Loftus D, Rask J, et.al., (2010), Earth Moon Planet 107:95–105. [10] Rask J, et.al., (2009) LEAG p 57. [11] Turci F, et.a., (2015), Astrobiology. 2015;15(5):371-380. [12] Hendrix DA, et.al., (2019), Geohealth. 2019;3(1):28-42. [13] Li, S., et.al., (2020), Science advances, 6(36), p.eaba1940. [14] Rask J. (2018), In: Cudnik B. (eds) Encyclopedia of Lunar Science. Springer, Cham. [15] Rask, J, (2020), LPI, Artemis III Sci. def. paper 2120.

chemical reactivity↗
NASA NTRSDate not supplied

EVA Swab Kit: Tools and Techniques for Collecting Aseptic Samples from Crewed Space Missions

Introduction: When we send humans to search for life on other planets, we'll need to know what we brought with us versus what may already be there. To ensure our crewed spacecraft meet planetary protection requirements—and to protect our science from human contamination—we'll need to assess and verify whether micro-organisms may be leaking/venting from our spacesuits. This requires collecting samples under Extravehicular Activity (EVA) conditions. Detailed, systematic research on forward contamination from robotic spacecraft has been steadily progressing since the Viking missions, but systematic studies of contamination from space suits has not been conducted in many years. The modern EMU (Extravehicular Mobility Unit) suit used by NASA is designed to leak at rates as high as 100 cc/min. Before humans land on Mars there is a critical need to understand the types and quantities of microbes that could be introduced via space suits. The Human Forward Contamination Assessment team at NASA’s Johnson Space Center (JSC) has developed a prototype EVA swab tool [1,2,3,4] designed for use in space to sample cleaned and uncleaned space suits to determine the present day microbial load and eventually the rate of leakage. The ability to assess microbial leakage early in advanced space suit and life support system design cycles will help avoid costly hardware redesign later. Test Objectives: The primary objective of EMU testing was to characterize the type of micro-organisms typically found on or near selected suit pressure joints under suit differential pressure conditions. Most human-borne microbes can fit through a 0.5 to 1.0 µm gap. Knowing which joints are more likely to leak will inform hardware design decisions. Knowing which types of micro-organisms may leak from EVA suits provides a basis for subsequent studies to characterize the viability of those organisms under destination conditions, as well as how far they might spread through natural or human-influenced processes. That data, in turn, will inform exploration mission operations and hardware design. The secondary objective of testing was to evaluate the interface between a fully suited test subject and the EVA swab tool at vacuum. Bulky EVA suits can restrict movement and limit visibility through the helmet visor. Fully suited testing is important for identifying tool design issues prior to flight. At exploration destinations, such as Mars, suited crew may be required to periodically sample their suits as part of an environmental monitoring protocol. Suit Microbial Sampling Results: This report details results of microbial swabs collected from current flight suit configurations worn by crew members assigned to upcoming ISS expedition missions as well as swabs collected from prototype suits intended for use on the Orion spacecraft. These tests were intended to characterize the types of contaminants found on flight suits under current, typical handling conditions. No attempt was made to change suit handling procedures, provide additional sterilization, or to limit typical potential contaminant sources. Using culture based techniques, we cultivated 235 CFU (colony forming units) comprised of 26 bacterial species and one fungal species on the outside of the suits. The fungal species and 14 of the bacterial species were unique to the suit surfaces and were not detected in any of the background samples collected within the chambers. We sequenced 755,434 ribosomal fragments on all of the suit surfaces from swab samples. 557,016 of these sequences represent DNA that survived at least 4 hours at vacuum. These sequences formed 2,464 OTU's (Operational Taxonomic Units, 97% similarity) showing low diversity in the samples. The most abundant sequences that survived vacuum belong to the genera Staphyloccocus, Ralstona, Bacillus and Rhodobacter all of which are common to the human microbiome. [5] See Danko et al., (2021) for more complete details of these first analyses. Further analysis of EVA suit materials with respect to the efficacy of various cleaning protocols and engineered containment solutions is planned to inform suit design for NASA’s Artemis Moon to Mars program crew testing. Swab Tool Function Results: The kit was demonstrated for fit and function in suited subject vacuum tests to determine how well the tool worked as an aseptic microbial sampling device as well as to identify any design elements that could be upgraded for EVA task specific improvement. It was found that sample acquisition efficacy could be enhanced by redesign of the sample canister to end-effector interface. Several modifications of the sample caddy assemblies to optimize EVA safety and functionality were also identified. Consequently, fabrication of the redesigned sample canister to end-effector assembly interfaces and and the sample caddy assemblies are required. Fabrication of sixteen flight sample canister assemblies (8 per each of two EVA Swab Kits) and two sample caddy assemblies are in process to be followed by hardware testing and certification to produce two flight-certified EVA Swab Kits for transport to ISS no earlier than summer of 2022. Sampling Strategy: The International Space Station is an ideal testbed for systematic studies of contamination from crewed vehicles since it has been continuously occupied for 20 years and exposed to non-terrestrial conditions. We will sample the exterior of the ISS during EVA using a purpose-built swab tool capable of maintaining sterility while undergoing temperature changes from -151 to +121°C under hard vacuum. Prior to each EVA, the project team will work with ISS mission managers to identify precise sampling locations, which will vary by EVA based on the translation paths and worksites scheduled for that particular EVA. Ideally, translation path handrails and areas near ECLSS (Environmental Control and Life Support System) external vent openings on a spacecraft would be assessed. There are currently more than a dozen ECLSS external vents on the ISS. Some are connected to systems that vent waste products, while others are intended to equalize cabin pressure. As EVA opportunity allows, microbial samples from any of these external vents would provide a valuable data point, though some will be more useful than others. Four criteria have been identified to help prioritize sampling sites near vents: • EVA Accessibility: To minimize cost, it is desired to piggy-back onto a planned EVA. Therefore, the sampling location must be readily accessible by an EVA crew • Type of Vented Products: Vent products that have been in direct contact with crew, such as cabin air, are more likely to contain microorganisms than vent products associated with isolated systems, such as experiment module combustion products. • Mass of Vented Products: Higher-flow vents are more likely to contain detectible levels of microbial contaminants than lower-flow vents. • Local Environment: Sample locations with relatively benign local conditions, such as warm surfaces shielded from direct ultraviolet (UV) radiation exposure, may be more likely to support microbial growth than locations with harsher local environmental conditions. Because EVA accessibility is the most important criteria, the proposal team worked with an astronaut and flight controllers using the Dynamic Onboard Ubiquitous Graphics (DOUG) tool. The DOUG virtual environment allows an operator to “fly” around the current ISS vehicle configuration to assess EVA translation paths, attach points, and keep-out zones. While analysis on station or rapid return to Earth would be preferable, samples collected from the exterior of the ISS have already been exposed to temperature variations between -157 and +121 °C as well as hard vacuum. Therefore, they should be fairly stable and robust. We hypothesize that samples collected from the ISS exterior could be stored for up to 6 months at -80°C without degradation. Sample canisters will be returned to Earth while frozen at -80°C for analysis, and sterilized canisters can be re-flown back to ISS to support additional sampling opportunities Relevance to NASA Exploration Objectives: These data will allow us to identify new or improved methods, technologies, and procedures for spacecraft sterilization and leakage mitigation to minimize the amount of contamination introduced to the environment by human explorers. This work is funded by NASA research grant: NNH18ZDA001N-PPR References: [1] Bell, M.S. et al. (2015) LPS XLVI, Abst. #1832 [2] Rucker et al. (2018) 42nd COSPAR (PPP.3) [3] Bell, M.S. et al. (2019) Mars Extant Life Conference, Abst. #5096.[4] Bell, M.S. et al., (2020) 43rd COSPAR (BO.2).[5] Danko D, et.al.,(2021)Front.Microbiol.12:608478.

Mary Suzanne Bell↗